Metal halide salts such as magnesium chloride have been demonstrated to be promising candidates for ammonia storage materials for energy storage and agriculture applications due to their ability to incorporate several moles of ammonia per mole of salt. Ammonia exiting a synthesis reactor can be separated from nitrogen and hydrogen by absorption into magnesium chloride. Such an absorption can be more complete and hotter than separation via ammonia condensation, the current standard in the Haber−Bosch process. Here, we discuss the optimal conditions for the cyclic uptake and release of ammonia from the supported magnesium chloride absorbents. An automated system was designed for measuring the nonequilibrium working capacity of the absorbent, as well as the impact of important operating conditions such as absorption and desorption temperature, pressure, and desorption time. Measurements of absorption and desorption kinetics provide insight into the mechanisms involved. The temperatures and pressures during absorption and desorption were designed to use minimal energy input to maximize the uptake and release of ammonia within a reasonable amount of time. In a laboratory-scale bed, absorption has a small unused bed length, so it is largely independent of mass transfer; it is dominated by how fast ammonia is fed to the bed. On the other hand, desorption is restricted both by the speed of heating the bed and by diffusion out of the absorbent. These measurements provide guidelines for ammonia separations and cycling sorbent materials on a larger scale.

While solid and liquid energy carriers are advantageous due to their high energy density, many do not meet the efficiency requirements to outperform hydrogen. In this work, we investigate ammonium formate as an energy carrier. It can be produced economically via a simple reaction of ammonia and formic acid, and it is safe to transport and store because it is solid under ambient conditions. We demonstrate an electrochemical cell that decomposes ammonium formate at 105 °C, where it is an ionic liquid. Here, hydrogen evolves at the cathode and formate oxidizes at the anode, both with ca. 100% Faradaic efficiency. Under the operating conditions, ammonia evaporates before it can oxidize; a second, modular device such as an ammonia fuel cell or combustion engine is necessary for complete oxidation. Overall, this system represents an alternative class of electrochemical fuel ionic liquids where the electrolyte is majority fuel, and it results in a modular release of hydrogen with potentially zero net-carbon emissions.

Knowledge of critical properties, such as critical temperature, pressure, density, as well as acentric factor, is essential to calculate thermo-physical properties of chemical compounds. Experiments to determine critical properties and acentric factors are expensive and time intensive; therefore, we developed a machine learning (ML) model that can predict these molecular properties given the SMILES representation of a chemical species. We explored directed message passing neural network (D-MPNN) and graph attention network as ML architecture choices. Additionally, we investigated featurization with additional atomic and molecular features, multitask training, and pretraining using estimated data to optimize model performance. Our final model utilizes a D-MPNN layer to learn the molecular representation and is supplemented by Abraham parameters. A multitask training scheme was used to train a single model to predict all the critical properties and acentric factors along with boiling point, melting point, enthalpy of vaporization, and enthalpy of fusion. The model was evaluated on both random and scaffold splits where it shows state-of-the-art accuracies. The extensive data set of critical properties and acentric factors contains 1144 chemical compounds and is made available in the public domain together with the source code that can be used for further exploration.

Decarbonization of long-haul trucks, which are the backbone of global supply chains, is necessary to meet climate goals. Currently, battery electric and conventional hydrogen powertrains are not cost-competitive solutions against diesel. Liquid Organic Hydrogen Carriers (LOHCs) are a promising fuel option that benefits from synergies with existing retail fuel distribution infrastructure, providing a cost-effective way to transport hydrogen energy. LOHCs are now used to deliver hydrogen gas to refueling stations, where it is then compressed and used to fuel trucks. However, this approach incurs ∼50% energy loss from the endothermic dehydrogenation and compression of hydrogen. We discuss an alternative concept based on onboard hydrogen release to address these pain points. We highlight recent advances in dehydrogenation reactor design, catalyst technologies, and hydrogen combustion engines that are relevant to the proposed system. Deficiencies in current technologies are discussed, along with potential research directions to address them. Initial analysis shows that the LOHC option, charged with blue hydrogen, achieves rough cost parity with diesel. The estimated well-to-wheel greenhouse gas emissions for this option are approximately one-third of diesel. Based on our analysis, LOHC-powered trucks featuring onboard dehydrogenation are a promising option to decarbonize long-haul trucking. However, making this option a reality will require dedicated study and development of core components for the power-dense, efficient, and robust onboard release of hydrogen from LOHCs along with efficient heat integration between the engine and the dehydrogenation reactor.